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Bounds on Parameters in Dynamic Discrete Choice Models

Author

Listed:
  • Bo E. Honoré

    (Department of Economics, University of Princeton)

  • Elie Tamer

    (Department of Economics, University of Princeton)

Abstract

Identification of dynamic nonlinear panel data models is an important and delicate problem in econometrics. In this paper we provide insights that shed light on the identification of parameters of some commonly used models. Using this insight, we are able to show through simple calculations that point identification often fails in these models. On the other hand, these calculations also suggest that the model restricts the parameter to lie in a region that is very small in many cases, and the failure of point identification may therefore be of little practical importance in those cases. Although the emphasis is on identification, our techniques are constructive in that they can easily form the basis for consistent estimates of the identified sets.

Suggested Citation

  • Bo E. Honoré & Elie Tamer, 2002. "Bounds on Parameters in Dynamic Discrete Choice Models," CAM Working Papers 2004-23, University of Copenhagen. Department of Economics. Centre for Applied Microeconometrics, revised Aug 2004.
    • Handle: RePEc:kud:kuieca:2004_23
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    File URL: http://www.econ.ku.dk/cam/wp0910/wp0203/2004-23.pdf/
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    Citations

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    Cited by:

    1. Richiardi Matteo & Poggi Ambra, 2012. "Imputing Individual Effects in Dynamic Microsimulation Models. An application of the Rank Method," Department of Economics and Statistics Cognetti de Martiis. Working Papers 201213, University of Turin.
    2. Timothy Halliday, 2006. "Identifying State Dependence in Non-Stationary Processes," Working Papers 200601, University of Hawaii at Manoa, Department of Economics.
    3. Timothy Halliday, 2007. "Testing for State Dependence with Time-Variant Transition Probabilities," Econometric Reviews, Taylor & Francis Journals, vol. 26(6), pages 685-703.
    4. Martin Browning & Jesus M. Carro, 2010. "Heterogeneity in dynamic discrete choice models," Econometrics Journal, Royal Economic Society, vol. 13(1), pages 1-39, February.
    5. Arie Beresteanu & Francesca Molinari, 2008. "Asymptotic Properties for a Class of Partially Identified Models," Econometrica, Econometric Society, vol. 76(4), pages 763-814, July.
    6. Matteo Richiardi & Ambra Poggi, 2014. "Imputing Individual Effects in Dynamic Microsimulation Models. An application to household formation and labour market participation in Italy," International Journal of Microsimulation, International Microsimulation Association, vol. 7(2), pages 3-39.
    7. Bo E. Honoré & Adriana Lleras-Muney, 2006. "Bounds in Competing Risks Models and the War on Cancer," Econometrica, Econometric Society, vol. 74(6), pages 1675-1698, November.
    8. Richard Blundell & Amanda Gosling & Hidehiko Ichimura & Costas Meghir, 2007. "Changes in the Distribution of Male and Female Wages Accounting for Employment Composition Using Bounds," Econometrica, Econometric Society, vol. 75(2), pages 323-363, March.
    9. Stéphane Bonhomme & Ulrich Sauder, 2009. "Accounting for Unobservables in Comparing Selective and Comprehensive Schooling," Working Papers wp2009_0906, CEMFI.
    10. Carro, Jesus M., 2007. "Estimating dynamic panel data discrete choice models with fixed effects," Journal of Econometrics, Elsevier, vol. 140(2), pages 503-528, October.

    More about this item

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities

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